SoroushVahidi/scidocs huggingface dataset:
Conflict in Adult Close Relationships : An Attachment Perspective
(2007)

Relationship researchers have focused on the frequency of conflict in couples' relationships and the manner in which couples engage in and try to resolve conflicts. conflict, under some conditions, may facilitate the development and maintenance of associated with patterns of behavior (e.g., negative affect reciprocity, demand-withdraw) and thought that tend to escalate conflict and make it more difficult to negotiate a facilitates intimacy or exacerbates distress may depend on individual differences in the way in which people interpret and respond to conflict. framework for understanding different responses to conflict. People are thought to differ in their working models of attachment, which include expectations, beliefs, and goals These working models are likely to shape people's thoughts, feelings, and behavior during conflict. For example, a person who expects close others to be generally responsive and available is likely to interpret and respond to conflict very differently from a person who expects close others to be rejecting and unavailable. Attachment theory may be able to inform the literature on 3 conflict in close relationships by suggesting how individuals might differ in how they construe conflict. At the same time, the study of relationship conflict provides a useful context for testing important aspects of attachment theory. Conflict may be particularly likely to reveal attachment processes because (a) it may act as a stressor on the relationship and thereby activate the attachment system (Simpson, Rholes, & Phillips, 1996), (b) it challenges partners' abilities to regulate their emotions and behavior (Kobak & Duemmler, 1994), which are thought to be connected to attachment processes and (c) it may trigger behaviors (e.g., personal disclosures) that typically promote intimacy, thereby providing evidence relevant to different attachment goals such as achieving intimacy or maintaining self-reliance (Pietromonaco & Feldman Barrett, 1997). In this chapter, we first discuss how conflict can be conceptualized within an attachment framework. Specifically, we propose that conflict may pose a threat to the attachment bond, but that it also may provide an opportunity for perceiving or experiencing greater intimacy. Furthermore, the degree to which people perceive conflict as a threat, opportunity, or both will depend on the content (e.g., expectations, beliefs, goals) of their working models of attachment. Next, we identify a set of predictions that follow from this framework, and evaluate extent to which empirical findings support these predictions; in particular, we attempt to integrate divergent findings in the empirical literature. Finally, we outline several critical …
Evaluation of cross-language voice conversion based on GMM and straight
(2001)

Voice conversion is a technique for producing utterances using any target speakers’ voice from a single source speaker’s utterance. In this paper, we apply cross-language voice conversion between Japanese and English to a system based on a Gaussian Mixture Model (GMM) method and STRAIGHT, a high quality vocoder. To investigate the effects of this conversion system across different languages, we recorded two sets of bilingual utterances and performed voice conversion experiments using a mapping function which converts parameters of acoustic features for a source speaker to those of a target speaker. The mapping functions were trained using bilingual databases of both Japanese and English speech. In an objective evaluation using Mel cepstrum distortion (Mel CD), it was confirmed that the system can perform cross-language voice conversion with the same performance as that within a single-language.
A Comparison Between the Silhouette Index and the Davies-Bouldin Index in Labelling IDS Clusters
(2006)

One of the most difficult problems in the design of an anomaly b sed intrusion detection system (IDS) that uses clustering is th at of labelling the obtained clusters, i.e. determining which of them correspond t ”good” behaviour on the network/host and which to ”bad” behaviour. In this pap er, a new clusters’ labelling strategy, which makes use of a clustering quality index is proposed for application in such an IDS. The aim of the new labelling algor ithm is to detect compact clusters containing very similar vectors and these are highly likely to be attack vectors. Two clustering quality indexes have been te sted and compared: the Silhouette index and the Davies-Bouldin index. Experiment al results comparing the effectiveness of a multiple classifier IDS with the two in dexes implemented show that the system using the Silhouette index produces sli ghtly more accurate results than the system that uses the Davies-Bouldin index. However, the computation of the Davies-Bouldin index is much less complex th an the computation of the Silhouette index, which is a very important advantage regarding eventual real-time operation of an IDS that employs clustering.
Detecting social media mobile botnets using user activity correlation and artificial immune system
(2016)

With the rapidly growing development of cellular networks and powerful smartphones, botnets have invaded the mobile domain. Social media, like Twitter, Facebook, and YouTube have created a new communication channel for attackers. Recently, bot masters started to exploit social media for different malicious activity, such as sending spam, recruitment of new bots, and botnet command and control. In this paper we propose a detection technique for social mediabased mobile botnets using Twitter. The proposed method combines the correlation between tweeting and user activity, such as clicks or taps, and an Artificial Immune System detector, to detect tweets caused by bots and differentiate them from tweets generated by user or by user-approved applications. This detector creates a signature of the tweet and compares it with a dynamically updated signature library of bot behavior signatures. The proposed system has been fully implemented on Android platform and tested under several sets of generated tweets. The test results show that the proposed method has a very high accuracy in detecting bot tweets with about 95% detection ratio.
A miniaturized wideband dual-polarized linear array with balanced antipodal Vivaldi antenna
(2016)

Previous studies on Vivaldi wideband phased arrays are mainly focused on Two-dimensional scanning phased arrays. A miniaturized balanced antipodal Vivaldi antenna (BAVA) is presented in this paper. A novel vertical parasitic metal strip loading is employed in the dual-polarized linear phased arrays, it to make a compact structure. With the arc-shaped slots and metal strip loads, the radiation performance in lower operating band can be greatly enhanced. The proposed antenna is simulated in the infinite condition through periodic boundary with the size of 100mm (length) *100mm (width) *125mm (depth). The antenna achieves an impedance bandwidth when scanning to ±50° for VSWR≤3 achieves 4:1 (0.5GHz-2GHz) bandwidth and 5:1 (0.4GHz-2GHz) bandwidth in the vertical polarization and horizontal polarization, respectively, and the isolation is less than -18dB over the operating frequency range.
The effects of stimulant therapy, EEG biofeedback, and parenting style on the primary symptoms of attention-deficit/hyperactivity disorder.
(2002)

One hundred children, ages 6-19, who were diagnosed with attention-deficit/hyperactivity disorder (ADHD), either inattentive or combined types, participated in a study examining the effects of Ritalin, EEG biofeedback, and parenting style on the primary symptoms of ADHD. All of the patients participated in a 1-year, multimodal, outpatient program that included Ritalin, parent counseling, and academic support at school (either a 504 Plan or an IEP). Fifty-one of the participants also received EEG biofeedback therapy. Posttreatment assessments were conducted both with and without stimulant therapy. Significant improvement was noted on the Test of Variables of Attention (TOVA; L. M. Greenberg, 1996) and the Attention Deficit Disorders Evaluation Scale (ADDES; S. B. McCarney, 1995) when participants were tested while using Ritalin. However, only those who had received EEG biofeedback sustained these gains when tested without Ritalin. The results of a Quantitative Electroencephalographic Scanning Process (QEEG-Scan; V. J. Monastra et al., 1999) revealed significant reduction in cortical slowing only in patients who had received EEG biofeedback. Behavioral measures indicated that parenting style exerted a significant moderating effect on the expression of behavioral symptoms at home but not at school.
Interval Stabbing Problems in Small Integer Ranges
(2009)

Given a set I of n intervals, a stabbing query consists of a point q and asks for all intervals in I that contain q. The Interval Stabbing Problem is to find a data structure that can handle stabbing queries efficiently. We propose a new, simple and optimal approach for different kinds of interval stabbing problems in a static setting where the query points and interval ends are in {1, . . . , O(n)}. keywords: interval stabbing, interval intersection, rank space, static, discrete, point enclosure
(Leveled) Fully Homomorphic Encryption without Bootstrapping
(2014)

We present a novel approach to fully homomorphic encryption (FHE) that dramatically improves performance and bases security on weaker assumptions. A central conceptual contribution in our work is a new way of constructing leveled, fully homomorphic encryption schemes (capable of evaluating arbitrary polynomial-size circuits of a-priori bounded depth), without Gentry’s bootstrapping procedure.
 Specifically, we offer a choice of FHE schemes based on the learning with error (LWE) or Ring LWE (RLWE) problems that have 2 λ security against known attacks. We construct the following.
 (1) A leveled FHE scheme that can evaluate depth-L arithmetic circuits (composed of fan-in 2 gates) using O(λ. L3) per-gate computation, quasilinear in the security parameter. Security is based on RLWE for an approximation factor exponential in L. This construction does not use the bootstrapping procedure.
 (2) A leveled FHE scheme that can evaluate depth-L arithmetic circuits (composed of fan-in 2 gates) using O(λ2) per-gate computation, which is independent of L. Security is based on RLWE for quasipolynomial factors. This construction uses bootstrapping as an optimization.
 We obtain similar results for LWE, but with worse performance. All previous (leveled) FHE schemes required a per-gate computation of Ω(λ3.5), and all of them relied on subexponential hardness assumptions.
 We introduce a number of further optimizations to our scheme based on the Ring LWE assumption. As an example, for circuits of large width (e.g., where a constant fraction of levels have width Ω(λ)), we can reduce the per-gate computation of the bootstrapped version to O(λ), independent of L, by batching the bootstrapping operation. At the core of our construction is a new approach for managing the noise in lattice-based ciphertexts, significantly extending the techniques of Brakerski and Vaikuntanathan [2011b].
Are educational computer microgames engaging and effective for knowledge acquisition at high-schools ? A quasi-experimental study
(2011)

Curricular schooling can benefit from the usage of educational computer games, but it is difficult to integrate them in the formal schooling system. Here, we investigate one possible approach to this integration, which capitalizes on using a micro-game that can be played with a teacher’s guidance as a supplement after a traditional expository lecture followed by a debriefing. The game’s purpose is to reinforce and integrate part of the knowledge learnt during the lecture. We investigated feasibility of this approach in a quasi-experimental study in 70 min long seminars on the topic of animal learning at 5 classes at 4 different high-schools in the Czech Republic. Each class was divided to two groups randomly. After an expository lecture, the game group played a game called Orbis Pictus Bestialis while the control group received an extra lecture that used media-rich materials. The time allotment was the same in both groups. We investigated the immediate and one month delayed effects of the game on students’ knowledge reinforced and integrated by the game as well as on knowledge learnt during the expository lecture but not strengthened by the game. We also investigated students’ overall appeal towards the seminar and its perceived educational value. Data from 100 students were analysed. The results showed that a) the game-playing is comparable to the traditional form of teaching concerning immediate knowledge gains and has a significant medium positive effect size regarding retention, b) the gameplaying is not detrimental to information transmitted in the expository lecture but not strengthened by the game, c) perceived educational value and the overall appeal were high in the game group, nevertheless the perceived educational value was slightly lower in the game group comparing to the traditional group. Our results suggest that the proposed approach of harnessing educational computer games at high-schools is promising. 2011 Elsevier Ltd. All rights reserved.
Deep learning for health informatics: Recent trends and future directions
(2017)

Health informatics has emerged as a growing domain of interest among researchers world-wide owing to its major implications on society. Applications of machine learning in healthcare range from disease prediction to patient-level personalized services. The prevalence of big data in healthcare has paved the way for applications based on deep learning techniques in the past few years. This paper reviews recent trends and applications of deep learning applied to the healthcare domain. We highlight recent research work, identify challenges and suggest possible future directions that could be pursued further in this domain.
Phase-Based Methods for Heart Rate Detection Using UWB Impulse Doppler Radar
(2016)

Ultra-wideband (UWB) pulse Doppler radars can be used for noncontact vital signs monitoring of more than one subject. However, their detected signals typically have low signal-to-noise ratio (SNR) causing significant heart rate (HR) detection errors, as the spurious harmonics of respiration signals and mixed products of respiration and heartbeat signals (that can be relatively higher than heartbeat signals) corrupt conventional fast Fourier transform spectrograms. In this paper, we extend the complex signal demodulation (CSD) and arctangent demodulation (AD) techniques previously used for accurately detecting the phase variations of reflected signals of continuous wave radars to UWB pulse radars as well. These detection techniques reduce the impact of the interfering harmonic signals, thus improving the SNR of the detected vital sign signals. To further enhance the accuracy of the HR estimation, a recently developed state-space method has been successfully combined with CSD and AD techniques and over 10 dB improvements in SNR is demonstrated. The implementation of these various detection techniques has been experimentally investigated and full error and SNR analysis of the HR detection are presented.
Inspection of pole-like structures using a vision-controlled VTOL UAV and shared autonomy
(2014)

We present an approach for the inspection of vertical pole-like infrastructure using a vertical take-off and landing (VTOL) unmanned aerial vehicle and shared autonomy. Inspecting vertical structures, such as light and power distribution poles, is a time consuming, dangerous and expensive task with high operator workload. To address these issues, we propose a VTOL platform that can operate at close-quarters, whilst maintaining a safe stand-off distance and rejecting environmental disturbances. We adopt an Image based Visual Servoing (IBVS) technique using only two line features to stabilise the vehicle with respect to a pole. Visual, inertial and sonar data are used, making the approach suitable for indoor or GPS-denied environments. Results from simulation and outdoor flight experiments demonstrate the system is able to successfully inspect and circumnavigate a pole.
SIT: A Lightweight Encryption Algorithm for Secure Internet of Things
(2017)

The Internet of Things (IoT) being a promising technology of the future is expected to connect billions of devices. The increased number of communication is expected to generate mountains of data and the security of data can be a threat. The devices in the architecture are essentially smaller in size and low powered. Conventional encryption algorithms are generally computationally expensive due to their complexity and requires many rounds to encrypt, essentially wasting the constrained energy of the gadgets. Less complex algorithm, however, may compromise the desired integrity. In this paper we propose a lightweight encryption algorithm named as Secure IoT (SIT). It is a 64-bit block cipher and requires 64-bit key to encrypt the data. The architecture of the algorithm is a mixture of feistel and a uniform substitution-permutation network. Simulations result shows the algorithm provides substantial security in just five encryption rounds. The hardware implementation of the algorithm is done on a low cost 8-bit micro-controller and the results of code size, memory utilization and encryption/decryption execution cycles are compared with benchmark encryption algorithms. The MATLAB code for relevant simulations is available online at https://goo.gl/Uw7E0W. Keywords—IoT; Security; Encryption; Wireless Sensor Network WSN; Khazad
Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
(2018)

Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights. Considering that slot and intent have the strong relationship, this paper proposes a slot gate that focuses on learning the relationship between intent and slot attention vectors in order to obtain better semantic frame results by the global optimization. The experiments show that our proposed model significantly improves sentence-level semantic frame accuracy with 4.2% and 1.9% relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively1.
The effects of prosthetic foot stiffness on transtibial amputee walking mechanics and balance control during turning.
(2017)

BACKGROUND
Little evidence exists regarding how prosthesis design characteristics affect performance in tasks that challenge mediolateral balance such as turning. This study assesses the influence of prosthetic foot stiffness on amputee walking mechanics and balance control during a continuous turning task.


METHODS
Three-dimensional kinematic and kinetic data were collected from eight unilateral transtibial amputees as they walked overground at self-selected speed clockwise and counterclockwise around a 1-meter circle and along a straight line. Subjects performed the walking tasks wearing three different ankle-foot prostheses that spanned a range of sagittal- and coronal-plane stiffness levels.


FINDINGS
A decrease in stiffness increased residual ankle dorsiflexion (10-13°), caused smaller adaptations (<5°) in proximal joint angles, decreased residual and increased intact limb body support, increased residual limb propulsion and increased intact limb braking for all tasks. While changes in sagittal-plane joint work due to decreased stiffness were generally consistent across tasks, effects on coronal-plane hip work were task-dependent. When the residual limb was on the inside of the turn and during straight-line walking, coronal-plane hip work increased and coronal-plane peak-to-peak range of whole-body angular momentum decreased with decreased stiffness.


INTERPRETATION
Changes in sagittal-plane kinematics and kinetics were similar to those previously observed in straight-line walking. Mediolateral balance improved with decreased stiffness, but adaptations in coronal-plane angles, work and ground reaction force impulses were less systematic than those in sagittal-plane measures. Effects of stiffness varied with the residual limb inside versus outside the turn, which suggests that actively adjusting stiffness to turn direction may be beneficial.
Software-specific part-of-speech tagging: an experimental study on stack overflow
(2016)

Part-of-speech (POS) tagging performance degrades on out-of-domain data due to the lack of domain knowledge. Software engineering knowledge, embodied in textual documentations, bug reports and online forum discussions, is expressed in natural language, but is full of domain terms, software entities and software-specific informal languages. Such software texts call for software-specific POS tagging. In the software engineering community, there have been several attempts leveraging POS tagging technique to help solve software engineering tasks. However, little work is done for POS tagging on software natural language texts.
 In this paper, we build a software-specific POS tagger, called S-POS, for processing the textual discussions on Stack Overflow. We target at Stack Overflow because it has become an important developer-generated knowledge repository for software engineering. We define a POS tagset that is suitable for describing software engineering knowledge, select corpus, develop a custom tokenizer, annotate data, design features for supervised model training, and demonstrate that the tagging accuracy of S-POS outperforms that of the Stanford POS Tagger when tagging software texts. Our work presents a feasible roadmap to build software-specific POS tagger for the socio-professional contents on Stack Overflow, and reveals challenges and opportunities for advanced software-specific information extraction.
A Constraint-Based Approach to Constructing Continuous Cartograms
(1998)

We present a new constraint-based continuous area cartogram construction method that is unique in its ability to preserve essential cues for recognition of region shapes. It automatically achieves desired region areas while maintaining correct map topology. The algorithm is compared with a number of existing methods, and results are shown to be superior in both accuracy and preservation of shape recognition cues. Through hierarchical resolution, we first perform gross adjustments upon a coarsely resampled map and later refine the map at progressively higher levels of detail.
Learning Environmental Sounds with Multi-scale Convolutional Neural Network
(2018)

Deep learning has dramatically improved the performance of sounds recognition. However, learning acoustic models directly from the raw waveform is still challenging. Current waveform-based models generally use time-domain convolutional layers to extract features. The features extracted by single size filters are insufficient for building discriminative representation of audios. In this paper, we propose multi-scale convolution operation, which can get better audio representation by improving the frequency resolution and learning filters cross all frequency area. For leveraging the waveform-based features and spectrogram-based features in a single model, we introduce twophase method to fuse the different features. Finally, we propose a novel end-to-end network called WaveMsNet based on the multi-scale convolution operation and two-phase method. On the environmental sounds classification datasets ESC-10 and ESC-50, the classification accuracies of our WaveMsNet achieve 93.75% and 79.10% respectively, which improve significantly from the previous methods.